Integration of social interactions into media sharing

ABSTRACT

Embodiments for managing social commentary as applicable to social media by a processor. A spectrum of weights is assigned to individual portions of the social commentary as a function of a determined measured characteristic. Those of the individual portions having a higher weight than a predetermined threshold are selected to be displayed through the social media in a hierarchy corresponding to a position in the spectrum of weights, while those of the individual portions having a lower weight than the predetermined threshold are selected to be withheld from display. A deduplication operation is applied to individual portions of the social commentary. Those of the individual portions determined to be repetitive greater than a predetermined number are used to assist in formulating weights of the individual portions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.15/235,330, filed on Aug. 12, 2016, which Continuation of U.S. patentapplication Ser. No. 15/235,253, filed on Aug. 12, 2016, the contents ofwhich are incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to computing systems, and moreparticularly to, various embodiments for managing social commentary asapplicable to social media by a processor.

Description of the Related Art

The emergence, growth, and proliferation of so-called “social media” hasbeen somewhat of a phenomenon. A prominent form of media consumption andsocial interaction that is often integrated into many social mediaapplications is that of shared media, such as videos of a particularsubject that allow for collective commenting. As the market for sharedmedia has continued to expand, collective commenting has increased inprominence to become a focal point of interest, often times resulting inusers devoting more time browsing the comments and socially interactingthan viewing the shared media.

SUMMARY OF THE INVENTION

Various embodiments for managing social commentary as applicable tosocial media by a processor, are provided. In one embodiment, by way ofexample only, a method for managing social commentary as applicable tosocial media by a processor comprises assigning a spectrum of weights toindividual portions of the social commentary comprising interactivecomments associated with media content shared on the social media as afunction of a determined measured characteristic. A deduplicationoperation is applied to the individual portions of the social commentaryto remove those of the individual portions selected to be withheld fromdisplay such that only original, first-instance interactions ofrespective ones of the individual portions are rendered to a user duringa playback of the media content. Those of the individual portionsdetermined to be repetitive greater than a predetermined number are usedto assist in formulating weights of the individual portions and those ofthe individual portions similar with one another beyond a predeterminedthreshold are selected for the deduplication operation.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict only typical embodiments of the invention and are nottherefore to be considered to be limiting of its scope, the inventionwill be described and explained with additional specificity and detailthrough the use of the accompanying drawings, in which:

FIG. 1 is a block diagram depicting an exemplary cloud computing nodeaccording to an embodiment of the present invention;

FIG. 2 is an additional block diagram depicting an exemplary cloudcomputing environment according to an embodiment of the presentinvention;

FIG. 3 is an additional block diagram depicting abstraction model layersaccording to an embodiment of the present invention;

FIG. 4 is a flowchart diagram depicting an exemplary method forintegrating social interactions with shared media by a processor, inwhich various aspects of the illustrated embodiments may be realized;

FIG. 5 is a block/flow diagram depicting an exemplary flow of socialinteractions integrated with shared media by a processor, again in whichvarious aspects of the illustrated embodiments may be realized;

FIG. 6 is an additional flowchart diagram depicting an exemplary methodfor insertion of social commentary into shared media by a processor, inwhich various aspects of the illustrated embodiments may be realized;

FIG. 7 is an additional block/flow diagram depicting an exemplary flowof insertion of social commentary into shared media by a processor,again in which various aspects of the illustrated embodiments may berealized;

FIG. 8 is an additional flowchart diagram depicting an exemplary methodfor integration of social interactions and shared media content by aprocessor, again in which various aspects of the illustrated embodimentsmay be realized; and

FIG. 9 is an additional flowchart diagram depicting an exemplary methodfor insertion of social commentary into shared media content by aprocessor, again in which various aspects of the illustrated embodimentsmay be realized.

DETAILED DESCRIPTION OF THE DRAWINGS

In conventional settings where shared media is presented withaccompanying social interaction, such as a video posted on a socialnetwork with accompanying comments, the shared media in general iscompartmentalized apart from the commentary. Comments, in thesescenarios, were initially implemented to be additive in nature, ratherthan designed as an integral part of the social video sharing platform.As the popularity of shared media has increased, and with it, the numberof users that may view a particular video (for example, a video that has“gone viral” in nature), this compartmentalization of media and additivecommentary may become burdensome for the user, as the user may berelegated to pursuing comments as a separate activity. This scenario mayresult, for example, in the user resorting to scroll through hundreds,sometimes thousands, of comments and then back to the shared media tohave some idea of what the commentary is referencing in relation to themedia.

A need exists for a mechanism whereby social interactions and sharedmedia may be integrated, so that a user may participate in the socialinteractions and commentary yet also continue to view the presentationof the associated shared media, and the overall experience for the useris thereby enriched.

In view of the foregoing, the mechanisms of the illustrated embodimentsprovide for solutions to the aforementioned compartmentalization ofmedia and social interaction to the benefit of the participating user.To wit, the illustrated embodiments herein provide mechanisms fordisplaying and embedding relevant and coherent social commentary withinsocial media video environments as managed by a processor device. Thesemechanisms implement the insertion of the social interactions (e.g.,comment insertion) of desired or relevant social interactions based onvarious preferences (such as user preferences) within and concurrentlydisplayed with the shared media in a single display frame, or within orin close proximity with, the display frame of the shared media.

In one embodiment, the mechanisms of the present invention analyze thevideo content and/or the social interactions when determining the layoutof the concurrently displayed commentary and shared media, such thatdisplay of the social interactions does not obfuscate view by the userof the content of the shared media. For example, the shared media may beanalyzed in a variety of ways to determine layout, placement, duration,style (e.g., font size or color), or other determinations forappropriately and effectively rendering the social commentary inconjunction with the shared media content.

In addition to analyzing shared media for determining effectiveplacement of the social interactions, the mechanisms of the illustratedembodiments also analyze the social interactions themselves for variousqualities and characteristics as will be further described. Each of thecharacteristics of the social interactions are used to determine suchaspects as the relevance of the social interactions to the shared media,the quality of the social interactions, and the popularity of the socialinteractions. Again, various preferences (such as user preferences) maybe used to rank the social interactions, organize the socialinteractions, and as will be further described, filtering operations toweed out those interactions determined to be irrelevant, inappropriate,or even offensive to the user.

In one embodiment, the mechanisms of the illustrated embodiments mayincorporate various deduplication operations on the social interactions,such that those interactions determined to be duplicates are processedin a similar manner as duplicate data in a deduplication operation. Theduplicate interactions then are withheld from being presented to theuser, enriching the viewing experience by only presenting originalinteractions. The duplicate interaction information also has anattendant benefit of being used by the mechanisms of the illustratedembodiments to rate the social interactions for a certaincharacteristic. For example, a certain comment that is found to havemany duplicates in a comment thread may indicate the level ofpopularity, or relevancy, of the comment to viewers of the shared mediaas a whole. Accordingly, the deduplication information may also be used,along with the preferences information and analysis information, toorganize the social interactions for presentation to a particular user.

As previously indicated, the mechanisms of the illustrated embodimentsmay analyze the shared media to determine placement decisions. In oneembodiment, for example, the mechanisms of the illustrated embodimentsmay analyze the shared media to determine the temporal location of whena particular social interaction should be best displayed. Duringanalysis of the shared media content, locations within the media thatwould be most appropriate for injection of the social interactions maybe determined and mapped in order to avoid overlays that would obscurewhat are determined to be important aspects of the shared media.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,system memory 28 may include at least one program product having a set(e.g., at least one) of program modules that are configured to carry outthe functions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in system memory 28 by way of example, and not limitation,as well as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provides cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provides pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and, in the context of the illustratedembodiments of the present invention, various social interactionworkloads and functions 96. In addition, social interaction workloadsand functions 96 may include such operations as media analytics,metadata analysis, and as will be further described, display managementfunctions. One of ordinary skill in the art will appreciate that thedata deduplication workloads and functions 96 may also work inconjunction with other portions of the various abstractions layers, suchas those in hardware and software 60, virtualization 70, management 80,and other workloads 90 (such as data analytics processing 94, forexample) to accomplish the various purposes of the illustratedembodiments of the present invention.

Turning now to FIG. 4, an exemplary method 400 for management of socialinteractions in the context of shared media by a processor is depicted,in which various aspects of the present invention may be implemented.Method 400 begins (step 402) with the assignment of a spectrum ofweights to individual portions of social commentary as a function of adetermined measured characteristic (step 404).

In a subsequent step 406, a deduplication operation is performed tocommentary metadata to assist in calculating particular weights for eachindividual portion. In the context of the illustrated embodiment, higherweighted portions (e.g., above a predetermined weight threshold) maythen be selected for rendering/display to the user, while other, lowerweighted portions (e.g., lower than the predetermined weight threshold)may be withheld from display (step 408). The method 400 then ends (step410).

FIG. 5, following, is a block/flow chart diagram depicting an additionalexemplary embodiment 500 for management of social interactions/socialcommentary in the context of shared video, here again in which variousaspects of the present invention may be realized. In the depictedembodiment, a display frame 506 is shown having shared media 502accompanied by related comments 508. The subject of the shared mediaconcerns a furry animal 504 whose behavior has triggered an apparentviral internet phenomenon, with a number of continually increasingcomments from fans across the world.

As one of ordinary skill in the art will appreciate, the ordering anddisplay of the related comments 508 typically is performed inchronological order (i.e., when the comments were posted), along withreply comments to an original commenter. Comments 510, 512, and 516 area subset of perhaps thousands of comments that are received by, andposted for, this particular shared media 502.

In the depicted embodiment, comments are collected and weighted. Thecomment weighting may be performed as a function of any number ofdetermined measured characteristics. As will be further described, thecomments themselves may be cognitively analyzed for certain terms,themes, repetition, style, and other characteristics. Any or all ofthese characteristics, along with in some embodiments, informationgleaned from the user's own preferences, may be used to weightindividual comments in the overall comment thread.

In one embodiment, comments are collected and weighted in terms ofoptional “favorite” and “like” characteristics. As further comments arereceived, as well as additional “favorite” and “like” characteristicsare received, the weighting may increase or decrease as a function ofthe overall weighting algorithm used in a particular scenario. Previouslikes made of similar comments of other shared media may be analyzed. Aspreviously mentioned, information may also be obtained from a userprofile to indicate user preferences, such as an aversion to profanityor comments determined to be politically themed.

As shown in FIG. 5, a so-called “cognitive operation” is performed onthe weighted comments as referenced by step 518. The cognitive operationmay include a wide variety of analytical functionality, but all designedto examine the content of the social interactions and shared media toobtain contextual information about the social interaction.

In one embodiment, the cognitive operation is performed on all commentsto provide a meaningful understanding of the comments beyond standarddictionary definitions. The benefit of performing a cognitive operationlies in the fact that many languages, including English, include wordsthat may have significantly different meanings based upon the contextthat the words are used. In addition to the social interactions, acognitive interpretation may occur on the audio portion of video-basedshared media, to again provide a meaningful understanding of the audiocontent beyond standard dictionary definitions. Here again, this benefitlies in how words may refer to vastly differing meanings depending onthe context.

Cognitive interpretations may also be conducted on the images of theshared media themselves, to again provide a meaningful understanding. Inthis case, the cognitive interpretation of shared video, for example,may be beneficial to determine actions being taken in both theforeground and the background of the shared video. These aspects maycome into importance as in many scenes the activity taking place in thebackground may be as important as the actions taking place in theforeground. In one embodiment, a temporal alignment process will beconducted and assist in the overall analysis and interpretation of theshared media.

From the social interaction tracking and cognitive data collection andanalysis operations previously just described in step 518, there may besituations where many similar comments, social interactions, and/oraspects of the shared media are found. In order to reduce storageutilization, and enhance the viewing experience for a user, theseinstances may be distilled to what are labeled as “unique instances.”

As part of what will become a data deduplication operation applied tothe individual portions of the social interactions, the number of uniqueinstances in a particular set of social interactions and/or shared mediaare tracked. Those social interactions that are determined to be similarenough in various aspects to be “duplicates” may then be deduplicated,or the duplicate social interaction may be removed. The number of uniqueinstances also assists in the weighting of social interactions from thestandpoint of relevancy and popularity. In one embodiment, socialinteractions may be analyzed across differing shared media (e.g.,different videos). In this way, the detection of inappropriate,repetitive (i.e., spamming) comments, and irrelevant comments may beidentified. These social interactions may then be removed fromconsideration and display.

In addition to tracking social interactions across differing sharedmedia, the user's interest may also be tracked across the social mediaplatform. For example, the user may be determined to have a biggerinterest in shared media concerning gardening techniques than sharedmedia concerning archery. These use preferences may allow the system togive additional weight to certain social interactions, certain sharedmedia, and/or portions thereof.

In one embodiment, overall interest in particular shared media may betracked by way of “favorites” and “likes” being applied to uniqueinstances within the media, as well as the deduplicated repeat ofanalyzed social interactions. The data gathered from the user interestcan then be compared against the data gathered from the overall mediainterest. If a correlation is found, regression can be used to calculatethe probability of the user's interest in that particular socialinteraction.

In addition to the foregoing, based on various preferences, such asthose gleaned from the user profile, previously analyzed activities, andother preferences, specific social interactions may be filtered so asnot to display to particular users. In other words, those socialinteractions deemed to be most applicable and desired by the user may beselected by the system for display to a particular user.

In some embodiments, an anti-spam filter may be applied to the socialinteractions to reduce irrelevant information. In other embodiments,other filters may be applied as will be appreciated by one of ordinaryskill in the art, and as will be further described. For example, basedupon data from the user profile, previous activities, and preferences;specific comments may be filtered so as not to display to particularusers. The comments deemed to be most applicable and desired by the userwill be selected. Once the social interactions are analyzed, weighted,filtered, and ordered, the social interactions may be displayed in anymanner or fashion, including traditional comment display under video, ormore advanced rendering methods according to a particular situation.

In some embodiments, for users with appropriate video capture/cameracapabilities, facial recognition/expression and eye tracking analysismay also be incorporated to assist in weighting individual socialinteractions. For example, biometric information may be obtained toindicate whether a user retains interest, her interest is piqued, or herinterest wanes during the viewing of certain shared media content orviewing and/or participating in social interaction.

Step 520 indicates various portions of the analysis previouslymentioned, where social interaction, following the cognitive operationsperformed and data obtained, is then appropriately deduplicated,filtered, and the remaining interactions weighted. Once the relevant,qualified social interactions are identified, then another illustratedset of functionality occurs as various conditions present and identifiedin the social interaction are identified and extracted from thecommentary in step 522. The system determines in the depicted embodimentthat the selected relevant comment should be displayed at timestamp 2:33to the user in step 524. The system compares the extracted conditionswith those analyzed from the shared media in real time. If theconditions are present in the shared media (decision step 526), then theappropriate social interaction (in this case, comment) 532 is displayed(step 528) as comment 530, in conjunction and concurrently with theshared media 502. If, however the conditions are not currently present,then the system may hold the comment (step 534) until the conditions aredeemed present, or the system may discard the comment.

It should be noted that the display of the comment 530 as shownconcurrently and in conjunction with the shared media 502 incorporatesadditional display functionality according to the mechanisms of theillustrated embodiments that will be further described, following. Aswill be shown in additional illustrated embodiments, the functionalityfor concurrent display of the shared media 502 and comment 530incorporates additional analysis, such that the display of theappropriate, relevant social interaction is also displayed for theappropriate duration in the appropriate location in the display 506,such that the display of the social interactions does not obfuscateviewing of any of the content of the shared media 502.

Turning first to FIG. 6, an exemplary method 600 is depicted forinsertion and display of social interactions in shared media by aprocessor, here again in which various aspects of the illustratedembodiments may be realized. Method 600 begins (step 602) with themapping of social interactions in real time according to a predeterminedpreference (step 604). Those social interactions determined to besimilar enough to be duplicate interactions are then managed accordingto a data deduplication operation (step 606).

In a subsequent step 608, the shared media is analyzed in view of themapped social interactions to generate placement and/or durationdecisions for rendering the content of the social interactions to a useron a particular display. Accordingly, once the display decisions aremade, the social interactions are then rendered concurrently with sharedmedia such that the content of the shared media is not obfuscated (step610). The method 600 then ends (step 612).

FIG. 7, following, is an additional block/flow diagram depicting variousaspects 700 of the present invention, including functionality forinjection/integration of social interactions with shared media by aprocessor. Here, as in FIG. 5, previously, a shared media 702 topic isbeing delivered to a user in a particular display 712. In the depictedembodiment, the shared media itself is analyzed in step 706 for variouscharacteristics 704 that may influence, for example, the way aparticular social interaction should be displayed to the user, such aslocation, duration, style, or other features. Here, as before, overallvideo interest in the shared media may be tracked by way of “favorites”and “likes” being applied to unique instances within the shared media,as well as the identified and determined duplicated repeat of socialinteractions. Those social interactions identified as relevant pursuantto various analysis such as those previously mentioned (e.g., weightingoperations etc.) are obtained in step 708.

Each of the social interactions may be analyzed in terms of theindividual social interaction (e.g., 714) along with integratedconditions (shown by 710). In a subsequent step, various socialinteraction preferences, such as user display preferences may beobtained (step 716). The various display preferences options mayinclude, in the depicted example, floating the social interactions overshared media, rendering the social interactions below the shared media,rendering the social interactions at the top right corner of the sharedmedia, or auto-calculation of an appropriate display aspect (shown byblock 718).

Based upon data from the user profile, and preferences; specific socialinteractions may be filtered so as not to display to particular users,as will be further described. Social interactions can be selected priorto playing the shared media, or optionally provided by an externalsource. In one embodiment, based upon user profile settings, socialinteractions from specific commenters may be selected. Other preferencesmay also be used to select specific social interactions (represented bystep 720).

The selected social interactions may then be further analyzed in termsof how the social interactions can best relate temporally to the sharedmedia. As previously described in FIG. 5, previously, the socialinteractions, a time stamp may then be applied to each selected socialinteraction. If, in one embodiment, a pre-determined comment positionpreference has been identified by the user, the selected socialinteractions will be placed accordingly as the social interactions alignwith a corresponding temporal location within the video. If nopre-determined comment position preference has been identified,locations within the shared media may then be analyzed for the bestcomment placement location that does not detract from the context of theshared media.

In one embodiment, display durations of the social interactions may bedetermined based on comment length, and a minimum and maximum displayduration may be assigned for each comment. “Best” placement locationsmay be determined by analyzing temporal segments of the video to ensurecomment placement and comment duration does not result in obfuscation ofthe video content. If, as previously and similarly described in FIG. 5,the various display conditions are determined to match the conditionsfound in the individual social interaction (decision step 722), then thesocial interaction is then rendered in step 724 concurrently with and/orwithin the shared media (designated by media set 728, illustrating bothshared media shown alongside comment 726) on the particular display 712.

Returning to step 722, if in real time, the particular condition in thevideo does not match that and/or those in the particular socialinteraction, then the system may hold the social interaction in step 730until that condition/those conditions are met, and the particular socialinteraction is rendered for the user. Alternatively, the system maydiscard the comment in a particular scenario (e.g., the comment containsa condition that has been determined to not present itself for theremaining duration of the shared media).

Turning now to FIGS. 8 and 9, following, additional flowchart diagramsshowing methods 800 and 900 are provided to further illustrate variousaspects of the present invention, including analyzing socialinteractions, deduplication of those social interactions determined tobe similar enough to warrant such an operation, analysis of sharedvideo, use of preferences, including user preferences obtained from auser profile, and other inventive aspects.

First, turning to FIG. 8, method 800 begins (step 802) with theinitialization of the user profile (step 804). A user may enter, orotherwise provide demographic information, preference information (e.g.,as to display preferences, content preferences, style, context, or otherpreferences), and the like. The profile information may be stored, forexample in a data repository in one or more distributed computingcomponents within a cloud-based computing environment such as thatpreviously depicted.

In a subsequent step, social interactions (in the depicted example,various comments regarding a posted video) are analyzed in real timethrough a cognitive operation to identify various contentcharacteristics in a manner described previously (step 806). Thecomments are then passed through a data deduplication operation to ratethose comments in terms of similarity, weed out duplicate comments andidentify unique instances as previously described (step 808). One ofordinary skill in the art will appreciate that the data deduplicationoperation performed on the comments may proceed in a manner similar tothose current procedures performed in other data storage scenarios, inlocal or distributed computing environments, etc.

If a comment is found to be duplicate in decision step 810, theduplicate comment is then removed, and the appropriate weight of theremaining comment is incremented to indicate the comment's popularityand/or relevancy, for example (step 812). As previously mentioned, otheranalyses may occur on the social interactions, where determinations asto relevancy, popularity, quality, or other characteristics may takeplace. If the comment is determined to not meet a predeterminedthreshold for these characteristics, such as a predetermined thresholdof comment quality (based on a pre-set quality algorithm, for example),or relevancy, or contain offensive or profane material not in accordancewith a user preference, for example, or the comment has been analyzed tobe repetitive across other shared media (e.g., to indicate that thecomment is spam) (decision step 814), then the comment is passed througha various filter (e.g., an anti-spam filter) and eliminated fromconsideration in step 816.

Throughout this process, and while depicted as a subsequent step in step818, the user profile may be consulted for various preferences toprovide additional context to a final calculated weight of anyindividual set of comments. This calculated weight may be a combinedfunction of the user preference(s), quality, relevance, duplicity, othermeasured characteristics, and other factors that are measurable throughthe cognitive process previously mentioned and calculable in analgorithm. In the depicted embodiment, the calculated weight for aparticular comment may be a function of user preferences, calculatedoverall interest in the shared media, calculated user interest (i.e., inthe instant shared media and/or over a number of shared media), andregression analysis (step 820).

Once the foregoing analysis is made, the remaining comments may beordered according to the various factors, such as by determined quality,relevance, and/or popularity above a predetermined threshold (step 822).In addition, the comments may be organized in terms of the variousconditions found in both the comments and the shared media. For example,a condition may be noted in an individual comment that another usercommented about a beauty mark on the snout of a particular pet dog thatmany users have commented as “cute.” This condition has been analyzed toappear in the shared at a specified time. Accordingly, the system thenorganizes the relevant beauty mark comments that have been selected fordisplay to this particular user to appear at approximately theidentified specified time in the shared media.

At the determined time, the selected comment(s) are then rendered to theuser concurrently with the video on the particular display (step 824).The rendered comments may then be analyzed in the context of collectedbiometric feedback from the user (e.g., eye tracking, audio and visualcues, etc.) (step 826). If the biometric feedback is determined to bepositive (e.g., the user's interest continues to remain active or ispiqued) (step 828), then the method ends (step 832). Alternatively, themethod 800 may reorder, insert other comments, and/or eliminate commentsper the biometric analysis in real time (step 830), returning to step826 to re-analyze the user's biometric information for additionalfeedback.

Turning now to FIG. 9, an additional flowchart diagram of an exemplarymethod 900 for insertion/integration/concurrent display of socialinteractions with shared media is depicted, here again in which variousaspects of the illustrated embodiments may be realized. Method 900begins (step 902) with the initialization of the user profile, aspreviously described. In a subsequent step 904, the shared media (e.g.,video) is analyzed for various preferences (step 906), and theassociated social interactions are managed through a deduplicationoperation (step 908). If similar social interactions are determined suchthat they are duplicates (decision step 910), the duplicates are removed(step 912).

In a subsequent step, the initialized user profile is consulted for oneor more preferences (step 914) that the user has saved, or the systemmay have learned over time by watching and recording various userbehaviors, for example. The appropriate display (e.g., location,duration, style, placement, etc.) parameters are then determined in viewof the shared media analysis, user preferences information, socialinteraction analysis, or other factors (step 916).

An alignment process is the performed on the shared video and socialinteractions (i.e., media set) in view of the display decisions thatwere generated (step 918). Various placement, duration, or other displayproperties for a particular display (e.g., taking into considerationresolution, video quality, screen size, computational requirements ofthe shared media and available hardware and software resources) are thengenerated (step 920). The social interactions are then renderedconcurrently with the shared media in the appropriate placement,duration, location, and other qualities to provide an enhanced viewingsituation for the particular user, but without obfuscation of thecontent of the shared media at a particular time (step 922). The method900 then ends (step 924).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowcharts and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowcharts and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowcharts and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowcharts or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustrations, and combinations ofblocks in the block diagrams and/or flowchart illustrations, can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts or carry out combinations of special purposehardware and computer instructions.

1. A method for managing social commentary as applicable to social mediaby a processor, comprising: assigning a spectrum of weights toindividual portions of the social commentary comprising interactivecomments associated with media content shared on the social media as afunction of a determined measured characteristic; and applying adeduplication operation to the individual portions of the socialcommentary to remove those of the individual portions selected to bewithheld from display such that only original, first-instanceinteractions of respective ones of the individual portions are renderedto a user during a playback of the media content, wherein those of theindividual portions determined to be repetitive greater than apredetermined number are used to assist in formulating weights of theindividual portions and those of the individual portions similar withone another beyond a predetermined threshold are selected for thededuplication operation.
 2. The method of claim 1, further including,applying, in conjunction with the deduplication operation, a commoncontent filter to the social commentary.
 3. The method of claim 1,further including applying, in conjunction with the deduplicationoperation, an anti-spam filter to the social commentary.
 4. The methodof claim 1, further including analyzing each of the individual portionsof the social commentary for a level of the determined measuredcharacteristic.
 5. The method of claim 1, further including determiningthe measured characteristic to indicate a level of relevance, a level ofquality, or a level of popularity of the social commentary to the socialmedia.
 6. The method of claim 1, further including initializing a userprofile.
 7. The method of claim 6, wherein initializing the user profilefurther includes recording a declared user preference to assist informulating the weights of the individual portions.
 8. The method ofclaim 7, further including determining whether a correlation is foundbetween a calculated overall interest in the social media and acalculated user interest in the social media based on information in theuser profile.
 9. The method of claim 8, wherein if the correlation isfound, applying a regression operation to calculate a user probabilityof interest in content of the social media.
 10. The method of claim 1,further including applying a cognitive interpretation operation to: thesocial commentary, an audio portion of the social media, or a videoportion of the social media.
 11. The method of claim 1, furtherincluding analyzing biometric data obtained from a user to assist informulating the weights of the individual portions.
 12. A method formanaging social commentary as applicable to social media by a processor,comprising: weighting individual portions of the social commentarycomprising interactive comments associated with media content shared onthe social media to correspond with a hierarchical order in which theindividual portions of the social commentary are displayed concurrentlywith the social media over an elapsed time; wherein a data deduplicationoperation is applied to the individual portions of the social commentaryto remove those of the individual portions selected to be withheld fromdisplay such that only original, first-instance interactions ofrespective ones of the individual portions are rendered to a user duringa playback of the media content, those of the individual portions deemedrepetitive additive to a particular weight of a deduplicated one of theindividual portions and those of the individual portions similar withone another beyond a predetermined threshold being selected for thededuplication operation.
 13. The method of claim 12, further includinganalyzing the individual portions of the social commentary for ameasured characteristic indicating a level of relevance, a level ofquality, or a level of popularity of the social commentary to the socialmedia.
 14. The method of claim 13, further including applying acognitive interpretation operation to the social commentary, an audioportion of the social media, or a video portion of the social media.